Resolving Vertex AI generateContent Error: Unexpected Token DOCTYPE in Node.js

Understanding the Vertex AI generateContent DOCTYPE Error in Node.js

When integrating Google Cloud Vertex AI with Node.js, developers sometimes encounter unexpected errors, such as the "Unexpected Token DOCTYPE" issue. This error typically occurs when trying to run code that works fine with cURL, but fails within a Node.js environment. Such problems can be frustrating, especially for those new to Vertex AI.

The error message, which includes references to a DOCTYPE and invalid JSON, is often an indication that the response from the API is not in the expected format. This happens when HTML content is returned instead of the expected JSON response, making it difficult to process. In such cases, troubleshooting becomes essential.

While the error might point to issues within the code, such as improper request handling, it is crucial to also investigate potential setup problems, particularly when using Google Cloud CLI on a local machine, like Windows. Even though the Vertex AI documentation offers sample code, slight differences in environment setup can cause inconsistencies.

This article dives into the specifics of this error and explores common troubleshooting steps. We will examine code snippets, explore the potential causes, and offer practical solutions for developers facing the DOCTYPE error in Vertex AI's generateContent function.

Breaking Down the Vertex AI Script and Error Handling

The scripts provided earlier are designed to handle the process of generating content using Google Cloud Vertex AI in a Node.js environment. The main purpose of the script is to query the Vertex AI generative models, like "gemini-1.0-pro", by sending a user input and receiving the AI's response. However, when handling APIs, unexpected issues such as receiving HTML content instead of JSON can occur. This is where the critical methods and error-handling techniques come into play. The script ensures that the response is properly parsed into JSON format, preventing the "DOCTYPE error" issue mentioned.

The first solution focuses on error handling using the try...catch block. This ensures that if an error is encountered while calling the generateContent function, the script does not crash. Instead, the error is caught, and a meaningful message is logged to the console. This kind of robust error handling is especially important when dealing with external services like Google Cloud Vertex AI, where network issues or incorrect API responses may result in failures. Furthermore, the use of asynchronous functions ensures that the API call is properly handled without blocking other operations, which is essential for optimizing performance.

Another key element in the script is the use of result.json(), which is crucial for parsing the API's response into a usable format. The problem in this case arises because the API response is not always guaranteed to be in JSON format. By checking the Content-Type header, the second solution ensures that the response is actually in JSON format before attempting to parse it. This helps prevent the script from trying to parse an HTML error page (like the one in the DOCTYPE error) as JSON, which would lead to the "Unexpected token '<'" error.

In the third solution, the focus shifts to testing. Here, unit tests are implemented using the Mocha and Chai libraries. Unit testing is a critical step in ensuring that the code behaves as expected across different environments and scenarios. By stubbing the API calls, the tests can simulate various responses from the Vertex AI service, allowing developers to verify that the code can handle both success and error cases properly. This approach ensures that the final product is more resilient and reliable, as it has been tested for a range of possible outcomes.

// Solution 1: Handling Unexpected HTML Response with Correct Fetching
const { VertexAI } = require('@google-cloud/vertexai');
const vertexAI = new VertexAI({ project: 'your-project-id', location: 'your-location' });
const model = vertexAI.getGenerativeModel({ model: 'gemini-1.0-pro' });
async function run(command) {
  try {
    const result = await model.generateContent({ contents: [{ role: 'user', parts: command }] });
    const jsonResponse = await result.json();
    console.log(jsonResponse);
  } catch (error) {
    console.error('Error processing response:', error.message);
  }
}
run("What is the capital of India?");

// Solution 2: Checking Content-Type Header to Ensure JSON
const { VertexAI } = require('@google-cloud/vertexai');
const vertexAI = new VertexAI({ project: 'your-project-id', location: 'your-location' });
const model = vertexAI.getGenerativeModel({ model: 'gemini-1.0-pro' });
async function run(command) {
  try {
    const result = await model.generateContent({ contents: [{ role: 'user', parts: command }] });
    if (result.headers.get('content-type').includes('application/json')) {
      const jsonResponse = await result.json();
      console.log(jsonResponse);
    } else {
      console.error('Unexpected response format:', result.headers.get('content-type'));
    }
  } catch (error) {
    console.error('Error fetching content:', error.message);
  }
}
run("What is the capital of India?");

// Solution 3: Writing Unit Tests for Vertex AI with Mocha and Chai
const chai = require('chai');
const { expect } = chai;
const sinon = require('sinon');
const { VertexAI } = require('@google-cloud/vertexai');
describe('Vertex AI Generate Content', () => {
  it('should return valid JSON content', async () => {
    const vertexAI = new VertexAI({ project: 'test-project', location: 'test-location' });
    const model = vertexAI.getGenerativeModel({ model: 'gemini-1.0-pro' });
    const stub = sinon.stub(model, 'generateContent').returns(Promise.resolve({
      json: () => ({ response: 'New Delhi' }),
      headers: { get: () => 'application/json' }
    }));
    const result = await model.generateContent('What is the capital of India?');
    const jsonResponse = await result.json();
    expect(jsonResponse.response).to.equal('New Delhi');
    stub.restore();
  });
});

Understanding Vertex AI Response Issues in Node.js

When working with Google Cloud Vertex AI in Node.js, a key aspect to consider is how data is exchanged between the API and the application. One common issue, as seen in this scenario, is receiving an unexpected HTML response instead of the expected JSON format. This can lead to syntax errors, such as "Unexpected token DOCTYPE," which occur because the code tries to parse HTML as if it were JSON. The root cause is usually a misconfigured request, an incorrect API endpoint, or a problem with authentication.

Additionally, it’s important to remember that Node.js operates differently than command-line tools like cURL. While cURL directly interacts with the API over HTTP, Node.js makes use of packages like the Google Cloud SDK. These libraries add layers of abstraction, meaning extra error handling and validation are necessary to ensure proper data exchange. Setting up headers correctly, especially the "Content-Type" for both requests and responses, is critical for handling API calls smoothly.

Another factor that could lead to errors is network configuration or local environment settings. When running the Vertex AI SDK on a local machine, the CLI environment might behave differently than a cloud-based environment. Issues like local proxy settings, firewall configurations, or missing environment variables may affect the response from Vertex AI. Thus, developers should ensure that their local environment mimics the cloud environment as closely as possible to avoid inconsistencies when switching between them.